I doubt the CPU costs will be measurably different. I suggest watching: https://class.coursera.org/ml-003/lecture/36 https://class.coursera.org/ml-003/lecture/

I doubt the CPU costs will be measurably different. I suggest watching: https://class.coursera.org/ml-003/lecture/36 https://class.coursera.org/ml-003/lecture/36 and you can jump to 7 minutes if you don't want to watch the whole thing. The only difference is instead of merely doing the matrix multiplication of your observation times your coefficients (the scalar product of θ and x) you have to take the output and use it as the input to the logistic function. The matrix multiplication will grow in complexity with the number of non-zero features (either non-zero coefficients in your model or non-zero features in your observation, presumably the latter) and the evaluation of the sigmoid function will be constant time, and on many architectures will be a very small number of instructions. If you're operating in very small numbers of dimensions what you're doing may still be interesting but I'm not sure it qualifies as "Big Data," regardless of the number of available observations. After all, if you're producing something with actual utility, you'll probably converge sooner rather than later...